Machine Learning Techniques for Road Traffic Automatic Incident Detection Systems: A Review

Abstract

Recently, non-recurrent congestion caused by road traffic incidents has become a critical concern of road Traffic Management System (TMS). However, incidents can’t be predicted. Hence, modern cities deployed Automatic Incidents Detection Systems (AIDSs) to early detect incidents and to improving road traffic flow efficiency and safety. For this, many AIDS approaches based on Machine Learning (ML) techniques are proposed. Although several reviews about AIDS have been written, a review of ML techniques based incident detection systems is required. The purpose of this paper is to discuss the recent research contributions in automatic incidents detection systems based on ML techniques. To achieve this goal, a review and a comparison of data sources, datasets, techniques and detection performances in both freeway and urban roads are provided. Finally, the paper concludes by addressing the critical open issues for conducting research in the future as a proposal framework.